电子学报2025，Vol.53Issue(2)：581-594,14.DOI:10.12263/DZXB.20240324
共享超分的双分支遥感图像时空融合网络
Shared Super-Resolution Dual-Branch Network for Spatiotemporal Fusion of Remote Sensing Images
摘要
Abstract
In this paper,we analyze the fusion law of scene weak change region and type change region,the differ-ence of physical model and the complementarity of effect from spatial and temporal dimensions,and propose a shared super-resolution dual-branch(Shared Super-Resolution Dual-Branch,SSRDB)remote sensing image spatio-temporal fusion algo-rithm.The algorithm has the following three characteristics:(1)A complementary network framework is constructed.Al-though the framework is an end-to-end deep learning model,each module has its own physical meaning and task.By adding intermediate supervision,the super-resolution modeling of spatial dimension,the change prediction modeling of time dimen-sion and the fusion modeling of the two advantages are realized respectively.(2)The mathematical representation of the change prediction is deduced,and a nonlinear compensation module is used to make the two branches share the super-reso-lution module.Under the dual strategy of sharing super-resolution module and recursive multiplexing super-resolution unit,the network parameters are significantly reduced.(3)The recursive super-resolution module uses fixed 2-magnification su-per-resolution units to gradually enhance features and reconstruct images under effective supervision and reference,which can effectively improve the precision of super-resolution,and flexibly adapt to spatio-temporal fusion tasks with different magnification differences by adjusting the number of super-resolution units.The SSRDB algorithm shows excellent fusion effect in spatial and spectral characteristics and change regions.The three quantitative evaluation indexes of RMSE(Root Mean Squared Error)、SAM(Spectral Angle Mapper)and SSIM(Structural Similarity)show that it is superior to the sub-optimal method on the CIA(Coleambally lrrigation Area)dataset by 7.067%,2.065%and 0.563%,respectively.On the LGC(Lower Gwydir Catchment)dataset,it is superior to the sub-optimal method by 5.319%,5.490%and 1.455%,respec-tively.On the Nanjing dataset,it is superior to the suboptimal method by 6.486%,16.290%and 0.481%,respectively.关键词
遥感图像/时空融合/双分支/图像超分/卷积神经网络Key words
remote sensing image/spatiotemporal fusion/dual-branch/image super-resolution/convolutional neural network分类
信息技术与安全科学引用本文复制引用
方帅,张小溪,张晶..共享超分的双分支遥感图像时空融合网络[J].电子学报,2025,53(2):581-594,14.基金项目
国家自然科学基金(No.61872327,No.61175033) National Natural Science Foundation of China(No.61872327,No.61175033) （No.61872327,No.61175033）
